Mukherjee Teesta, Pournik Omid, Lim Choi Keung Sarah N, Arvanitis Theodoros N
Department of Electronic, Electrical and Systems Engineering, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
Cancers (Basel). 2023 Jul 6;15(13):3523. doi: 10.3390/cancers15133523.
CDSSs are being continuously developed and integrated into routine clinical practice as they assist clinicians and radiologists in dealing with an enormous amount of medical data, reduce clinical errors, and improve diagnostic capabilities. They assist detection, classification, and grading of brain tumours as well as alert physicians of treatment change plans. The aim of this systematic review is to identify various CDSSs that are used in brain tumour diagnosis and prognosis and rely on data captured by any imaging modality. Based on the 2020 preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, the literature search was conducted in PubMed and Engineering Village Compendex databases. Different types of CDSSs identified through this review include Curiam BT, FASMA, MIROR, HealthAgents, and INTERPRET, among others. This review also examines various CDSS tool types, system features, techniques, accuracy, and outcomes, to provide the latest evidence available in the field of neuro-oncology. An overview of such CDSSs used to support clinical decision-making in the management and treatment of brain tumours, along with their benefits, challenges, and future perspectives has been provided. Although a CDSS improves diagnostic capabilities and healthcare delivery, there is lack of specific evidence to support these claims. The absence of empirical data slows down both user acceptance and evaluation of the actual impact of CDSS on brain tumour management. Instead of emphasizing the advantages of implementing CDSS, it is important to address its potential drawbacks and ethical implications. By doing so, it can promote the responsible use of CDSS and facilitate its faster adoption in clinical settings.
临床决策支持系统（CDSSs）正在不断发展并融入日常临床实践，因为它们可协助临床医生和放射科医生处理大量医学数据，减少临床错误，并提高诊断能力。它们有助于脑肿瘤的检测、分类和分级，还能提醒医生注意治疗方案的变化。本系统评价的目的是识别用于脑肿瘤诊断和预后且依赖任何成像方式获取的数据的各种CDSS。根据2020年系统评价和Meta分析的首选报告项目（PRISMA）方案，在PubMed和工程索引数据库中进行了文献检索。通过本次评价确定的不同类型的CDSS包括Curiam BT、FASMA、MIROR、HealthAgents和INTERPRET等。本评价还考察了各种CDSS工具类型、系统特征、技术、准确性和结果，以提供神经肿瘤学领域的最新证据。本文提供了此类用于支持脑肿瘤管理和治疗中临床决策的CDSS的概述，以及它们的益处、挑战和未来展望。尽管CDSS提高了诊断能力和医疗服务水平，但缺乏支持这些说法的具体证据。实证数据的缺乏减缓了用户对CDSS对脑肿瘤管理实际影响的接受度和评估。与其强调实施CDSS的优势，不如关注其潜在的缺点和伦理影响。这样做可以促进对CDSS的合理使用，并促进其在临床环境中的更快采用。
